Load-Balanced Trees for Data Collection in Wireless Sensor Networks

Huang, Chung-Kuo; Chang, Guey Yun; Sheu, Jang‐Ping

doi:10.1109/icppw.2012.65

Cited by 6 publications

(1 citation statement)

References 7 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These studies can be broadly classified into tree-based approaches, integer programming approaches, time allocation approaches and flow based schemes [18]. In tree-based data collection approaches [19]- [21], a data collection tree, constructed in an energy efficient manner is used for the convergecast. The integer programming approach formulates the lifetime maximization problem as an integer 0-1 programming problem [22].…”

Section: Introductionmentioning

confidence: 99%

Maximum Lifetime Convergecast Tree in Wireless Sensor Networks

John¹,

Kasbekar²,

Baghini³

2019

Preprint

View full text Add to dashboard Cite

We study the problem of building a maximum lifetime data collection tree for periodic convergecast applications in wireless sensor networks. We experimentally observe that if two nodes transmit the same number of data packets, the amount of energy consumption of the nodes is approximately same even if the payload lengths of the transmitted packets are different. The major energy consumption during a packet transmission arises from radio start-up and medium access control overhead. Our formulated lifetime maximization problem captures the energy expenditure due to message transmissions/receptions in terms of the number of data packets in contrast to prior works, which consider data units (amount of sensor data generated by a node). Variable transmission power levels of the radio and accounting for the sensor energy consumption are other factors that make our problem formulation different from those in prior work. We prove that this problem is NP-complete by reducing the set cover problem to it and propose an algorithm to solve it. The performance of the proposed algorithm is experimentally evaluated using Jain's fairness index as a metric by implementing it on an actual testbed consisting of 20 sensor nodes and compared with those of the widely used shortest path tree and random data collection tree algorithms. The energy consumption of different nodes under the proposed algorithm are shown to be more balanced than under the shortest path tree and random data collection tree algorithms. Hence, the proposed algorithm provides a higher network lifetime than that under the other two algorithms.

show abstract